Sensor-Actor-Networks (SANET) consist of several heterogeneous subsystems, which provide specific capabilities for measuring or manipulating its environment. During the runtime, the communication infrastructure as well as communication tasks and the available communication resources are changing dynamically. Furthermore, advanced application scenarios in this domain have strict requirements reg...

We consider a network of service-providing agents, where different agents have different capabilities, availability, and cost to solve problems. These characteristics are particularly important in practice for semi-automated call centers which provide quality customer service in real time. We have developed SANet, a service agent network for call center automation, to serve as an experimental t...

In a call center, service agents with diierent capabilities are available for solving incoming customer problems at any time. To supply quick response and better problem solution to customers, it is necessary to schedule customer problems to appropriate service agents ee-ciently. We developed SANet, a service agent network for call center, which integrates multiple service agents including both...

Miniaturization, reduced costs of electronic components, and advanced information technologies now open practical possibilities to design, develop and deploy thousands of the coin-sized sensors and mechanical devices at multiple locations. This kind of softwarehardware systems, pervasively available to the user in everyday activities, is named Ubiquitous Computing Environment (UCE) (Abowd & Myn...

This paper presents our Complex Answer PAragraph Retrieval (CAPAR) system designed for our participation in the TREC Complex Answer Retrieval (CAR) track. Because we were provided with a massive training set consisting of complex questions as well as the paragraphs that answered each aspect of the complex question, we cast the paragraph ranking as a learning to rank (L2R) problem, such that we ...

In recent years, deep learning methods have been widely used for road extraction in remote sensing images. However, the existing semantic segmentation networks generally show poor continuity due to high-class similarity between roads and buildings surrounding images, existence of shadows occlusion. To deal with this problem, paper proposes strip attention (SANet) extracting Firstly, a module (S...

Accurate polyp segmentation is of great importance for colorectal cancer diagnosis. However, even with a powerful deep neural network, there still exists three big challenges that impede the development segmentation. (i) Samples collected under different conditions show inconsistent colors, causing feature distribution gap and overfitting issue; (ii) Due to repeated downsampling, small polyps a...

Sea-land segmentation of remote sensing images is great significance to the dynamic monitoring coastlines. However, types objects in coastal zone are complex, and their spectra, textures, shapes, distribution features different. Therefore, sea-land for various coastlines still a challenging task. In this article, scale-adaptive semantic network, called SANet, proposed images. SANet has made two...